Topic: foundation models

Oden Institute Seminar by Rex Ying (Yale University) on how non-Euclidean geometries, particularly hyperbolic geometry, can enhance foundation models by better capturing hierarchies and symmetries in real-world data, with applications across Transformers, language model training, multimodal systems, and recommender systems.

EMBO | EMBL Symposium bringing machine-learning and life-science researchers together across sequencing, imaging and structural biology. Topics include foundation models, cross-modality learning, causal explanation, mechanistic interpretability, generative models and AI agents. Onsite workshops and tutorials take place on the first day; the following three days offer hybrid talks and posters. Registration is not yet open; the official page accepts expressions of interest.

Lead computational work on protein structure, design and antibody discovery. Develop methods that connect protein foundation models, antigen and binder datasets, biophysical prediction and experimental results, while building reliable data workflows and GPU-enabled tools. The position includes team leadership and collaboration with laboratory scientists. This hybrid role advertises USD 200,000–250,000. Applications are currently available without a fixed closing date.

Ai2's Robotics team is hiring a research scientist to investigate foundation models for robotics and develop open methods, datasets, benchmarks, and models. The work combines large-scale simulation with real robots, spanning manipulation, planning and control, reinforcement or imitation learning, sim-to-real transfer, world models, and broadly competent robotic systems.

For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. This is called dynamical systems reconstruction (DSR), the learning o

Andreas Tolias discusses foundation models trained on large neuroscience datasets and their potential to generalize across experimental settings.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-tra